How are neural networks built in practice?
A &quot;software library&quot; is a codebase that includes many commonly used functions/algorithms, you do not need to write the same function again if you &quot;import&quot; the library.
In practice, practitioners/researchers don't often code the nitty gritty details of well-used algorithms, they just use existing libraries such as sklearn or TensorFlow that already has an existing implementation.
In the case of sklearn, ignoring some details, you can create a muti-layer perceptron in this line of code:
&gt;&gt;&gt; clf = MLPClassifier(activation = 'relu', alpha=1e-5, hidden_layer_sizes((5,2))

...in which clf is an &quot;object&quot; that refers to the neural network, MLPClassifier is a function provided by sklearn that &quot;creates&quot; a neural net depending on the parameters provided.
In this case, the parameters provided are that the activation function is ReLU, where the network has two layers, with the first having 5 nodes, and the second has 2 nodes.
All other parameters that are not explicitly provided are set to a default value, in this case, the learning rate, among others.
It should be noted that this is not complete yet, we still have not completed training. Luckily, this is as simple as:
&gt;&gt;&gt; clf.fit(X, y)

Where X and y are some array-shaped data/target set.
I recommend sklearn as i think its more beginner friendly.
https://scikit-learn.org/stable/getting_started.html